Identifying and patching vulnerabilities of camera-LiDAR based Autonomous Driving Systems

Autonomous driving systems rely on advanced perception models to interpret their surroundings and make real-time driving decisions. Among these, Bird’s Eye View (BEV) perception has emerged as a critical component, offering a unified 3D representation from multi-camera and sensor inputs. While BEV-based models have gained traction in industry-leading platforms, their security vulnerabilities remain largely underexplored in adversarial machine learning research. This study provides a multi-dimensional security analysis of BEV perception models, focusing on adversarial threats in both vision-only and multi-sensor fusion architectures. We examine the susceptibility of state-of-the-art models - including BEVDet, BEVDet4D, DAL, and BEVFormer—to adversarial attacks targeting their detection and decision-making capabilities. Unlike traditional adversarial research that primarily misleads perception models at the classification level, this study investigates real-world attack scenarios where adversaries can manipulate perception to cause practical disruptions, such as inducing traffic congestion or triggering unsafe vehicle behaviors. Our findings reveal significant security risks in BEV-based perception, with both vision-only and sensor-fusion models vulnerable to adversarial perturbations. Attack transferability across architectures further highlights the urgency of developing robust defense mechanisms to ensure the reliability of self-driving technology. This work underscores the need for adversarially resilient perception models to safeguard the future of autonomous driving.

• Record URL:
  • https://www.clemson.edu/cecas/tracr/documents/projects/final-reports/ccihang-report.pdf

Language

• English

Project

• Status: Completed
• Funding: $112393
• Contract Numbers:

  69A3552344812

  69A3552348317

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  University of California, Santa Cruz

  1156 High Street, Mail Stop SOE2
  Santa Cruz, California  United Kingdom  95064

  University of Texas at Dallas

  800 W Campbell Rd
  Richardson, Texas  United States  75080
• Managing Organizations:

  National Center for Transportation Cybersecurity and Resiliency (TraCR)

  Clemson University
  Clemson, SC  United States 

  University of California, Santa Cruz

  1156 High Street, Mail Stop SOE2
  Santa Cruz, California  United Kingdom  95064
• Project Managers:

  Chowdhury, Mashrur

• Performing Organizations:

  University of California, Santa Cruz

  1156 High Street, Mail Stop SOE2
  Santa Cruz, California  United Kingdom  95064

  University of Texas at Dallas

  800 W Campbell Rd
  Richardson, Texas  United States  75080
• Principal Investigators:

  Xie, Cihang

  Cardenas, Alvaro

  Kantarcioglu, Murat

• Start Date: 20240101
• Expected Completion Date: 20241231
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Cameras; Computer security; Laser radar; Machine learning; Physical phenomena; Safety; Sensors
• Subject Areas: Data and Information Technology; Planning and Forecasting; Safety and Human Factors; Security and Emergencies; Transportation (General); Vehicles and Equipment;

Filing Info

• Accession Number: 01906996
• Record Type: Research project
• Source Agency: National Center for Transportation Cybersecurity and Resiliency (TraCR)
• Contract Numbers: 69A3552344812, 69A3552348317
• Files: UTC, RIP
• Created Date: Feb 5 2024 4:04PM